Multi-layered perceptron network for short-term load forecasting
Bibliographic record
Abstract
Abstract The use of the multi-layer perceptron (MLP) network for short-term load forecasting is performed. Through weather and load data from the Hydro-Quebec database, capabilities, advantages, and limitations of this artificial intelligence method in load forecasting are investigated. Current management tools for energy systems are based on deterministic optimization methods, where supply, demand, and production are assumed to be known. Changes in electricity supply and demand have made their adjustment more complex. It is no longer a question of adjusting centralized production to demand, but rather of adjusting centralized production, decentralized production, and production from decentralized storage facilities. Our approach will be based on a predictive optimization method adapted to energy systems. An artificial neural network is applied to forecast load for Mascouche in Quebec, Canada. It is about a part of the Hydro-Quebec’s grid where the maximum capacity is 140 megawatts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".